Research Article
Classification of Epileptic and Non-Epileptic EEG Events
@INPROCEEDINGS{10.4108/icst.mobihealth.2014.257352, author={Evangelia Pippa and Evangelia Zacharaki and Iosif Mporas and Vasiliki Tsirka and Mark Richardson and Michael Koutroumanidis and Vasileios Megalooikonomou}, title={Classification of Epileptic and Non-Epileptic EEG Events}, proceedings={4th International Conference on Wireless Mobile Communication and Healthcare - "Transforming healthcare through innovations in mobile and wireless technologies"}, publisher={IEEE}, proceedings_a={MOBIHEALTH}, year={2014}, month={12}, keywords={epileptic seizures; pnes; vasovagal syncope; classification; machine learning}, doi={10.4108/icst.mobihealth.2014.257352} }- Evangelia Pippa
Evangelia Zacharaki
Iosif Mporas
Vasiliki Tsirka
Mark Richardson
Michael Koutroumanidis
Vasileios Megalooikonomou
Year: 2014
Classification of Epileptic and Non-Epileptic EEG Events
MOBIHEALTH
IEEE
DOI: 10.4108/icst.mobihealth.2014.257352
Abstract
In this paper, the classification of epileptic and non-epileptic events from multi-channel EEG data is investigated using a large number of time and frequency domain features. In contrast to most of the evaluations found in the literature, in this paper the non-epileptic class consists of two types of paroxysmal episodes of loss of consciousness namely the psychogenic non epileptic seizure (PNES) and the vasovagal syncope (VVS). For the classification, several classification algorithms were explored. The classification models were evaluated on EEG epochs from 11 subjects in an inter-subject cross-validation setting and the best among them achieved classification accuracies of 86% (Bayesian Network), 83% (Random Committee) and 74% (Random Forest).